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On the Vector Representation of Utterances in Dialogue Context

In recent years, the representation of words as vectors in a vector space, also known as word embeddings, has achieved a high degree of attention in the research community and the benefits of such a representation can be seen in the numerous applications that utilise it. In this work, we introduce dialogue vector models, a new language resource that represents dialogue utterances in vector space and captures the semantic meaning of those utterances in the dialogue context. We examine how the word vector approach can be applied to utterances in a dialogue to generate a meaningful representation of them in vector space. Utilising existing dialogue corpora and word vector models, we create dialogue vector models and show that they capture relevant semantic information by comparing them to manually annotated dialogue acts. Furthermore, we discuss potential areas of application for dialogue vector models, such as dialogue act annotation, learning of dialogue strategies, intent detection and paraphrasing.